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advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data, with a focus on identifying critical transformation windows and assessing phase evolution kinetics
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advance the various existing commercial and research technologies that are currently (or have future potential to be) employed for IPM of industrial metal AM machines, and developing new methods
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thomas.forbes@nist.gov 301.975.2111 Edward Ryan Sisco edward.sisco@nist.gov 301 975 2093 Description This opportunity focuses on developing and measuring the capabilities of ambient ionization mass spectrometry
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that can be integrated into the research workflows used in developing new materials (e.g., carbon nanotubes) or in determining disease pathologies (e.g., Alzheimer’s disease). We want to explore solutions
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areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research
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resolution techniques are explored to achieve quantitative reconstruction of nanoscale structure images by developing novel DUV/EUV imaging optics and quantitative phase retrieval algorithms. A qualified
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further enriches the available data from which material behavior can be extracted. Separate work is being done to develop robust algorithms to quantitatively compare the physical and simulated experimental
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algorithms to improve methods for peptide identification from raw mass spectral data. The use of orthogonal information such as multi-enzyme digestions, to verify the presence of a peptide using different
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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position